Applying the Recurrent Neural Network LSTM method to analyze the bid / ask info from the exchange's order book data. The final product is a combination of a visual display depicting real-time future market volatility for Bitcoin. This project can help retail investors become more aware of market risk while assisting traders in making more deliberate hedging decisions.
This is a final project done after 9 weeks of Data Science Bootcamp at Le Wagon.
Presented by Students Howard Li (Product Manager), Jessica Chuh (Full Stack Developer), and Jack Wu (Model Architect).